SES health gradients in late life: testing models of gene-environment interplay in an international twin consortium

《2020年健康人民》将消除健康结果中的不平等作为公共卫生优先事项。社会 健康分层是有据可查的，普遍存在的，并且越来越受到关注，因为它似乎是 随着时间的推移而增加。减少这些社会阶层的差距需要更好地理解社会如何 健康的影响比我们现在所拥有的。虽然大多数研究集中在个人层面的社会经济 地位-定义为社会地位，职业分类，教育和收入-新 研究已开始侧重于宏观经济环境。此外，尽管遗传（G）和 已知环境（E）因素有助于SES-健康梯度， 两组因素联合收割机影响健康结果（即，GE相互作用）知之甚少。模型 通用电气公司的相互作用在其环境重点方面有所不同（有毒环境与健康的疾病触发效应- 促进有利环境的益处）和预期的遗传对疾病的贡献（在 在不利的环境中，在有利的环境中，或在两个极端）了解是否高SES 优先促进遗传选择的个体子集中的良好健康（即，社会 增强），低社会经济地位是否会引发遗传易感人群的健康状况不佳（即， 素质-压力）或两者兼有，对于将这一领域的研究转化为有效的预防战略至关重要。 多项研究中基因与环境相互作用联盟（IGEMS）现有16项研究， 在美国进行的纵向双胞胎研究，瑞典、丹麦、芬兰和澳大利亚准备对这些项目进行评估 模型IGEMS包括超过52，000对单独的双胞胎，其中超过7000对同卵双胞胎 差异模型，以及超过12，600对双卵双胞胎和近4000对异性对的性别差异 模型样本涵盖了广泛的年龄范围（摄入时为15至103岁），包括一组特征良好的 纵向表型，包括身体健康的协调测量（例如，主观健康、慢性 疾病指标、体重指数、肺功能、血压、日常生活活动和握力）， 认知健康（语言能力、空间能力、记忆力和处理速度）和情绪健康 （抑郁，孤独）以及SES多个方面的测量（例如，职业、教育、金融 应变）。此外，IGEMS参与者的一个大的子集具有全基因组基因分型， 计算多基因风险评分（PRS）。我们将使用双胞胎控制/配对内模型，定量遗传学， 适度，性别限制模型和PRS分析，以调查SES健康的具体机制 个人水平以及国家和历史群组水平的梯度（例如，国家一级的 社会不平等）。这项拟议中的研究反映了研究人员的创新愿景， 通过新的数据收集解决研究问题-成功地建立了一个合作， 16项具有相关表型的大型国际研究，使这项研究具有高度的成本效益和效率。

Inequalities in health outcomes are targeted by Healthy People 2020 as a public health priority. The social stratification of health is well documented, pervasive, and of growing concern because it appears to be increasing over time. Reducing these social class disparities will require greater understanding of how social class impacts health than we currently have. While most research focuses on individual-level socioeconomic status—defined as social status that accrues to occupational classification, education, and income—new research has begun to focus on the macro-economic environment. Further, although both genetic (G) and environmental (E) factors are known to contribute to the SES-health gradient, the mechanisms by which the two sets of factors combine to influence health outcomes (i.e., GE interplay) are poorly understood. Models of GE interplay differ in their environmental focus (disease-triggering effects of toxic environments vs. health- promoting benefits of favorable environments) and the expected genetic contribution to disease (maximized in adverse environments, in favorable environments, or at both extremes) Understanding whether high-SES preferentially promotes good health among a genetically selected subset of individuals (i.e., social enhancement), whether low-SES triggers poor health among a genetically vulnerable subset of individuals (i.e., diathesis-stress), or both, is essential for translating research in this area into effective prevention strategies. The consortium on Interplay of Genes and Environments across Multiple Studies (IGEMS) with 16 existing longitudinal twin studies in the U.S., Sweden, Denmark, Finland, and Australia is poised to evaluate these models. IGEMS includes more than 52,000 individual twins, with over 7000 identical twin pairs for within-pair difference models, and over 12,600 dizygotic twin pairs with nearly 4000 opposite sex pairs for sex-difference models. The sample spans a wide age range (15 to 103 years at intake) and includes a set of well-characterized longitudinal phenotypes, including harmonized measures of physical health (e.g., subjective health, chronic disease indicators, body mass index, lung function, blood pressure, activities of daily living, and grip strength), cognitive health (verbal ability, spatial ability, memory, and processing speed), and emotional health (depression, loneliness) as well as measures of multiple facets of SES (e.g., occupation, education, financial strain). In addition, a large subset of IGEMS participants has genome wide genotyping from which we have computed polygenic risk scores (PRS). We will use co-twin control/within pair models, quantitative genetic moderation, sex-limitation models, and PRS analyses to investigate specific mechanisms of the SES-health gradient at the individual level as well as the country and historical cohort level (e.g., country-level indices of social inequality). The proposed study reflects an innovative vision by the investigators who—rather than addressing the research questions through new data collection—successfully created a collaboration among 16 large international studies with relevant phenotypes, making this study highly cost-effective and efficient.